nmma: An extended Bayesian framework for Nuclear Multimessenger Astronomy in the Era of Next-Generation Detectors

Context. The joint analysis of different (astro-)physical messengers, in particular gravitational-wave data and electromagnetic follow-up observations, allows us to establish, explore and deepen links between different physical fields. As new survey capacities and improved detection methods will lead to a significant increase in the number of multimessenger detections in the upcoming decades, efficient and versatile software frameworks are essential to maximise the scientific outcome of such multimessenger studies. Aims. We present a major upgrade to the Nuclear Multimessenger Astronomy (nmma) framework, incorporating various recent developments in theoretical modelling and machine learning in a modularised and easily extendable Bayesian framework. For the first time, this allows direct sampling on nuclear parameters alongside gravitational-wave, kilonova and afterglow parameters. Methods. We combine fast surrogate models for electromagnetic transients with speed-ups from emulators that map nuclear parameters to macroscopic neutron-star properties. Additional acceleration methods for the evaluation of state-of-the-art waveform approximants enable full Bayesian analyses of multimessenger events at the speed required in the era of next-generation detectors. Results. We demonstrate the capabilities of the upgraded nmma framework through a series of representative applications. Reanalysing the 2017 multi-messenger detection of a neutron-star merger, we achieve 20- to 60-fold speed-ups while using more detailed physical models compared to previous studies. Moreover, we demonstrate for a hypothetical future detection how we can simultaneously constrain nuclear parameters and the Hubble parameter with robustly quantified uncertainties.